def test_load_and_remap_matrix(self):
"""Tests the end-to-end loading / remapping of weights."""
# _load_and_remap_matrix() is the generalized wrapper that takes in row and
# column vocabulary files, calls the relevant remappings, and returns the
# weight matrix. Take this example to be linear multi-class by providing
# both row and column vocabularies.
remapped_matrix = checkpoint_ops._load_and_remap_matrix(
new_row_vocab_file=self.new_feature_vocab_file,
old_row_vocab_file=self.old_feature_vocab_file,
num_rows_to_load=4,
new_col_vocab_file=self.new_class_vocab_file,
old_col_vocab_file=self.old_class_vocab_file,
new_col_vocab_size=4,
old_tensor_name='some_scope/embeddings',
ckpt_path=[self.checkpoint_file],
new_row_vocab_offset=1,
initializer=self.initializer,
num_row_oov_buckets=1,
num_col_oov_buckets=1)
# [4 in vocab + 1 oov features, 4 in vocab + 1 oov classes]. The offset
# means we read from the first line.
expected_remapped_matrix = np.concatenate(
[
np.reshape([18, 34, 50, self.init_val, self.init_val], [5, 1]),
np.reshape([16, 32, 48, self.init_val, self.init_val], [5, 1]),
np.reshape([self.init_val] * 5, [5, 1]),
np.reshape([17, 33, 49, self.init_val, self.init_val], [5, 1]),
np.reshape([self.init_val] * 5, [5, 1])
],
axis=1)
with self.cached_session():
self.assertAllClose(expected_remapped_matrix,
self.evaluate(remapped_matrix))
def test_load_and_remap_matrix(self):
"""Tests the end-to-end loading / remapping of weights."""
# _load_and_remap_matrix() is the generalized wrapper that takes in row and
# column vocabulary files, calls the relevant remappings, and returns the
# weight matrix. Take this example to be linear multi-class by providing
# both row and column vocabularies.
remapped_matrix = checkpoint_ops._load_and_remap_matrix(
new_row_vocab_file=self.new_feature_vocab_file,
old_row_vocab_file=self.old_feature_vocab_file,
num_rows_to_load=4,
new_col_vocab_file=self.new_class_vocab_file,
old_col_vocab_file=self.old_class_vocab_file,
new_col_vocab_size=4,
old_tensor_name='some_scope/embeddings',
ckpt_path=[self.checkpoint_file],
new_row_vocab_offset=1,
initializer=self.initializer,
num_row_oov_buckets=1,
num_col_oov_buckets=1)
# [4 in vocab + 1 oov features, 4 in vocab + 1 oov classes]. The offset
# means we read
expected_remapped_matrix = np.concatenate(
[
np.reshape([18, 34, 50, self.init_val, self.init_val], [5, 1]),
np.reshape([16, 32, 48, self.init_val, self.init_val], [5, 1]),
np.reshape([self.init_val] * 5, [5, 1]),
np.reshape([17, 33, 49, self.init_val, self.init_val], [5, 1]),
np.reshape([self.init_val] * 5, [5, 1])
],
axis=1)
with self.test_session():
self.assertAllClose(expected_remapped_matrix, remapped_matrix.eval())
